Association Between Preoperative Ocular Parameters and Myopic Shift in Children Undergoing Primary Intraocular Lens Implantation.

Purpose: To evaluate the association between preoperative ocular parameters and myopic shift following primary intraocular lens (IOL) implantation in pediatric cataracts. Methods: Eyes from pediatric patients undergoing bilateral cataract surgery with primary IOL implantation were included. Eyes were grouped by age at surgery and subdivided into three axial length (AL) subgroups and three keratometry subgroups. Mixed-effects linear regression was utilized to assess the trend in myopic shift among subgroups. Multivariable analysis was performed to determine factors associated with myopic shift. Results: A total of 222 eyes were included. The median age at surgery was 4.36 years (interquartile range [IQR], 3.16-6.00 years) and the median follow-up was 4.18 years (IQR, 3.48-4.64 years). As preoperative AL increased, a decreased trend was observed in myopic shift and rate of myopic shift (P = 0.008 and P = 0.003, respectively, in the 4 to <6 years old group; P = 0.002 and P < 0.001, respectively, in the ≥6 years old group). Greater myopic shift and rate of myopic shift were associated with younger age at surgery (P = 0.008 and P = 0.008, respectively). Both myopic shift and rate of myopic shift were negatively associated with AL. Conclusions: Age at surgery and preoperative AL were associated with myopic shift in pediatric cataracts following primary IOL implantation. Adjusting the target refraction based on preoperative AL could potentially improve patients' long-term refractive outcome. Translational Relevance: This study may help to guide the selection of postoperative target refraction according to age at surgery and preoperative ocular parameters for pediatric cataracts.

Classification of congenital cataracts based on multidimensional phenotypes and its association with visual outcomes.

AIM: To establish a classification for congenital cataracts that can facilitate individualized treatment and help identify individuals with a high likelihood of different visual outcomes. METHODS: Consecutive patients diagnosed with congenital cataracts and undergoing surgery between January 2005 and November 2021 were recruited. Data on visual outcomes and the phenotypic characteristics of ocular biometry and the anterior and posterior segments were extracted from the patients' medical records. A hierarchical cluster analysis was performed. The main outcome measure was the identification of distinct clusters of eyes with congenital cataracts. RESULTS: A total of 164 children (299 eyes) were divided into two clusters based on their ocular features. Cluster 1 (96 eyes) had a shorter axial length (mean±SD, 19.44±1.68 mm), a low prevalence of macular abnormalities (1.04%), and no retinal abnormalities or posterior cataracts. Cluster 2 (203 eyes) had a greater axial length (mean±SD, 20.42±2.10 mm) and a higher prevalence of macular abnormalities (8.37%), retinal abnormalities (98.52%), and posterior cataracts (4.93%). Compared with the eyes in Cluster 2 (57.14%), those in Cluster 1 (71.88%) had a 2.2 times higher chance of good best-corrected visual acuity [<0.7 logMAR; OR (95%CI), 2.20 (1.25-3.81); P=0.006]. CONCLUSION: This retrospective study categorizes congenital cataracts into two distinct clusters, each associated with a different likelihood of visual outcomes. This innovative classification may enable the personalization and prioritization of early interventions for patients who may gain the greatest benefit, thereby making strides toward precision medicine in the field of congenital cataracts.

Cost-effectiveness and cost-utility of a digital technology-driven hierarchical healthcare screening pattern in China.

Utilization of digital technologies for cataract screening in primary care is a potential solution for addressing the dilemma between the growing aging population and unequally distributed resources. Here, we propose a digital technology-driven hierarchical screening (DH screening) pattern implemented in China to promote the equity and accessibility of healthcare. It consists of home-based mobile artificial intelligence (AI) screening, community-based AI diagnosis, and referral to hospitals. We utilize decision-analytic Markov models to evaluate the cost-effectiveness and cost-utility of different cataract screening strategies (no screening, telescreening, AI screening and DH screening). A simulated cohort of 100,000 individuals from age 50 is built through a total of 30 1-year Markov cycles. The primary outcomes are incremental cost-effectiveness ratio and incremental cost-utility ratio. The results show that DH screening dominates no screening, telescreening and AI screening in urban and rural China. Annual DH screening emerges as the most economically effective strategy with 341 (338 to 344) and 1326 (1312 to 1340) years of blindness avoided compared with telescreening, and 37 (35 to 39) and 140 (131 to 148) years compared with AI screening in urban and rural settings, respectively. The findings remain robust across all sensitivity analyses conducted. Here, we report that DH screening is cost-effective in urban and rural China, and the annual screening proves to be the most cost-effective option, providing an economic rationale for policymakers promoting public eye health in low- and middle-income countries.

Metabolomic profiling of the aqueous humor in patients with pediatric cataract.

Pediatric cataract, including congenital and developmental cataract, is a kind of pediatric vision-threatening disease with extensive phenotypic heterogeneity and multiple mechanisms. We aimed to investigate the metabolite profile of aqueous humor (AH) in patients with pediatric cataracts, and identify underlying mutual correlations between differential metabolites. Metabolomic profiles of AH were analyzed and compared between pediatric cataract patients (n = 33) and age-related cataract patients without metabolic diseases (n = 29), using global untargeted metabolomics with ultra-high-performance liquid chromatography tandem mass spectrometry. Principal component analysis, partial least squares discriminant analysis and heat map were applied. Enriched pathway analysis was conducted using Kyoto Encyclopedia of Genes and Genomes. Receiver-operating characteristic (ROC) analyses were employed to select potential biomarkers. A total of 318 metabolites were identified, of which 54 differential metabolites (25 upregulated and 29 downregulated) were detected in pediatric cataract group compared with controls (variable importance of projection >1.0, fold change ≥1.5 or ≤ 0.667 and P < 0.05). A significant accumulation of N-Acetyl-Dl-glutamic acid was observed in pediatric cataract group. The differential metabolites were mainly enriched in histidine metabolism (increased L-Histidine and decreased 1-Methylhistamine) and the tryptophan metabolism (increased N-Formylkynurenine and L-Kynurenine). 5-Aminosalicylic acid showed strong positive mutual inter-correlation with L-Tyrosinemethylester and N,N-Diethylethanolamine, both of which were down-regulated in pediatric cataract group. The ROC analysis implied 11 metabolites served as potential biomarkers for pediatric cataract patients (all area under the ROC curve ≥0.900). These results illustrated novel potential metabolites and metabolic pathways in pediatric cataract, which provides new insights into the pathophysiology of pediatric cataract.

Combination of serum markers with optical coherence tomography angiography for evaluating neuromyelitis optica spectrum disorders and multiple sclerosis.

BACKGROUND: Neuromyelitis optica spectrum disorder (NMOSD) and multiple sclerosis (MS), autoimmune inflammatory diseases of the central nervous system, affect the optic nerve and brain. A lumbar puncture to obtain biomarkers is highly invasive. Serum biomarkers and optical coherence tomography angiography (OCTA) are more accessible and less expensive than magnetic resonance imaging and provide reliable, reproducible measures of neuroaxonal damage. This study investigated the association between serum neurofilament light chain (sNfL), serum glial fibrillary acidic protein (sGFAP), and OCTA metrics. Serum sNfL and sGFAP levels, OCTA values, and clinical characteristics were compared among 91 patients with NMOSD, 81 patients with MS, and 34 healthy controls (HCs) at baseline and 1-year follow-up. RESULTS: sNfL and sGFAP levels were higher while the sGFAP/sNfL quotients were significantly lower in NMOSD and MS patients than those in HCs. At baseline, the average thicknesses of the peripapillary retinal nerve fibre layer (pRNFL) and macular ganglion cell-inner plexiform layer (mGC-IPL) were significantly smaller in NMOSD and MS patients than those in HCs (pRNFL: MS 92.0 [80.2; 101] µm, NMOSD 80.0 [59.0; 95.8] µm, vs HC 99.0 [92.0; 104] µm, p < 0.001; mGC-IPL: MS 74.5 [64.2; 81.0] µm, NMOSD 68.0 [56.0; 81.0] µm, vs HC 83.5 [78.0; 88.0] µm, p < 0.001). The vessel density (VD) and perfusion density (PD) were increased in MS patients without optic neuritis compared to HCs (VD: MS 16.7 [15.6; 17.9] HC 15.3 [13.4; 16.9], p = 0.008; PD: MS 0.41 [0.38; 0.43], HC 0.37 [0.32; 0.41], p = 0.017). In NMOSD patients without optic neuritis, sNfL was significantly associated with PD at baseline (r = 0.329, q = 0.041). The baseline and follow-up values of the sNfL level and average pRNFL and mGC-IPL thicknesses in MS patients showed significant differences. NMOSD patients showed significant differences between baseline and follow-up sNfL and sGFAP levels but not OCTA metrics. CONCLUSION: Changes in retinal microvasculature might occur earlier than those in retinal structure and may therefore serve as a promising diagnostic marker for early NMOSD. The combination of serum markers and OCTA metrics could be used to evaluate and differentiate between MS and NMOSD.

CRB1-associated retinal degeneration is dependent on bacterial translocation from the gut.

The Crumbs homolog 1 (CRB1) gene is associated with retinal degeneration, most commonly Leber congenital amaurosis (LCA) and retinitis pigmentosa (RP). Here, we demonstrate that murine retinas bearing the Rd8 mutation of Crb1 are characterized by the presence of intralesional bacteria. While normal CRB1 expression was enriched in the apical junctional complexes of retinal pigment epithelium and colonic enterocytes, Crb1 mutations dampened its expression at both sites. Consequent impairment of the outer blood retinal barrier and colonic intestinal epithelial barrier in Rd8 mice led to the translocation of intestinal bacteria from the lower gastrointestinal (GI) tract to the retina, resulting in secondary retinal degeneration. Either the depletion of bacteria systemically or the reintroduction of normal Crb1 expression colonically rescued Rd8-mutation-associated retinal degeneration without reversing the retinal barrier breach. Our data elucidate the pathogenesis of Crb1-mutation-associated retinal degenerations and suggest that antimicrobial agents have the potential to treat this devastating blinding disease.

Urban greenspace and visual acuity in schoolchildren: a large prospective cohort study in China.

BACKGROUND: Greenspace is known to have a positive impact on human health and well-being, but its potential effects on visual acuity have not been extensively studied. OBJECTIVES: Our aim was to examine the relationship between long-term greenspace exposure and visual acuity in children, while also exploring the potential mechanisms in this association. METHODS: We conducted this prospective cohort study based on the Children's growth environment, lifestyle, physical, and mental health development project (COHERENCE), which screened 286,801 schoolchildren in Guangzhou, China, starting in the 2016/17 academic year and followed them up for three academic years (2017/18-2019/20). Visual acuity was measured using a standardized logarithmic chart, and visual impairment was defined as visual acuity worse than 0.0 logarithm of the minimum angle of resolution (LogMAR) units in the better eye. We used the Normalized Difference Vegetation Index (NDVI), the Soil-Adjusted Vegetation Index (SAVI), and the Enhanced Vegetation Index (EVI) to assess the greenspace surrounding child's geocoded home and school at each visit. RESULTS: Our analysis indicated that higher greenspace exposure was associated with greater visual acuity z-score at baseline and with slower decline in visual acuity z-score during the 3-year follow-up. An interquartile range increase in home-school-based NDVI 300m was associated with a 7% decrease [hazard ratios (HRs): 0.93, 95% confidence interval (CI): 0.92, 0.94] in the risk of visual impairment. We also found that air pollution, physical activity, outdoor time, and recreational screen time partially mediated the greenspace-visual acuity association. CONCLUSION: Our findings suggest that increasing greenspace exposure could benefit children's visual acuity development and reduce the risk of visual impairment by reducing air pollution and recreational screen time while increasing physical activity and outdoor time. All results could have potential policy implications, given the individual and societal burdens associated with visual impairment.

Green Space Morphology and School Myopia in China.

Importance: China has experienced both rapid urbanization and major increases in myopia prevalence. Previous studies suggest that green space exposure reduces the risk of myopia, but the association between myopia risk and specific geometry and distribution characteristics of green space has yet to be explored. These must be understood to craft effective interventions to reduce myopia. Objective: To evaluate the associations between myopia and specific green space morphology using novel quantitative data from high-resolution satellite imaging. Design, Setting, and Participants: This prospective cohort study included students grades 1 to 4 (aged 6 to 9 years) in Shenzhen, China. Baseline data were collected in 2016-2017, and students were followed up in 2018-2019. Data were analyzed from September 2020 to January 2022. Exposures: Eight landscape metrics were calculated using land cover data from high-resolution Gaofen-2 satellite images to measure area, aggregation, and shape of green space. Main Outcome and Measures: The 2-year cumulative change in myopia prevalence at each school and incidence of myopia at the student level after 2 years were calculated as main outcomes. The associations between landscape metrics and school myopia were assessed, controlling for geographical, demographic, and socioeconomic factors. Principal component analyses were performed to further assess the joint effect of landscape metrics at the school and individual level. Results: A total of 138 735 students were assessed at baseline. Higher proportion, aggregation, and better connectivity of green space were correlated with slower increases in myopia prevalence. In the principal component regression, a 1-unit increase in the myopia-related green space morphology index (the first principal component) was negatively associated with a 1.7% (95% CI, -2.7 to -0.6) decrease in myopia prevalence change at the school level (P = .002). At the individual level, a 1-unit increase in myopia-related green space morphology index was associated with a 9.8% (95% CI, 4.1 to 15.1) reduction in the risk of incident myopia (P < .001), and the association remained after further adjustment for outdoor time, screen time, reading time, and parental myopia (adjusted odds ratio, 0.88; 95% CI, 0.80 to 0.97; P = .009). Conclusions and Relevance: Structure of green space was associated with a decreased relative risk of myopia, which may provide guidance for construction and renovation of schools. Since risk estimates only indicate correlations rather than causation, further interventional studies are needed to assess the effect on school myopia of urban planning and environmental designs, especially size and aggregation metrics of green space, on school myopia.

Early Diagnosis of Syndromic Congenital Cataracts in a Large Cohort of Congenital Cataracts.

PURPOSE: To explore the factors related to the diagnosis yield of syndromic congenital cataracts and describe the phenotype-genotype correlation in congenital cataract patients. DESIGN: Prospective cohort study. METHODS: Setting: the participants from underwent clinical examinations between 2021 and 2022. Facial and anterior eye segment photographs, pre- and postoperative ocular parameters, and medical and family histories were recorded. Bioinformatics analysis was performed using whole-exome sequencing data. Statistical and correlation analyses were performed using the basic characteristics, deep phenotype, and genotype data. PARTICIPANTS: 115 patients with unrelated congenital cataract. INTERVENTIONS: performing clinical examinations, whole-exome sequencing, and bioinformatics analysis for all participants. MAIN OUTCOMES AND MEASURES: factors related to the genetic diagnosis yield of syndromic congenital cataracts. RESULTS: Bilaterally asymmetrical cataracts were identified to be associated with syndromic congenital cataracts. The overall genetic diagnostic yield in the cohort was 72.2%. In total, 34.8% of the probands were early diagnosed with various syndromes with the help of genetic information. A phenotype-genotype correlation was detected for some genes and deep phenotypes. CONCLUSIONS: We highlight the importance of screening syndromic diseases in the patients with asymmetrical congenital cataracts. Application of whole-exome sequencing helps provide early diagnosis and treatment for the patients with syndromic congenital cataracts. This study also achieved a high genetic diagnostic yield, expanded the genotypic spectrum, and found phenotype-genotype correlations. A comprehensive analysis of cataract symmetricity, family history, and deep phenotypes makes the genotype prediction of some congenital cataract patients possible.

An interpretable model predicts visual outcomes of no light perception eyes after open globe injury.

BACKGROUND: The visual outcome of open globe injury (OGI)-no light perception (NLP) eyes is unpredictable traditionally. This study aimed to develop a model to predict the visual outcomes of vitrectomy surgery in OGI-NLP eyes using a machine learning algorithm and to provide an interpretable system for the prediction results. METHODS: Clinical data of 459 OGI-NLP eyes were retrospectively collected from 19 medical centres across China to establish a training data set for developing a model, called 'VisionGo', which can predict the visual outcome of the patients involved and compare with the Ocular Trauma Score (OTS). Another 72 cases were retrospectively collected and used for human-machine comparison, and an additional 27 cases were prospectively collected for real-world validation of the model. The SHapley Additive exPlanations method was applied to analyse feature contribution to the model. An online platform was built for real-world application. RESULTS: The area under the receiver operating characteristic curve (AUC) of VisionGo was 0.75 and 0.90 in previtrectomy and intravitrectomy application scenarios, which was much higher than the OTS (AUC=0.49). VisionGo showed better performance than ophthalmologists in both previtrectomy and intravitrectomy application scenarios (AUC=0.73 vs 0.57 and 0.87 vs 0.64). In real-world validation, VisionGo achieved an AUC of 0.60 and 0.91 in previtrectomy and intravitrectomy application scenarios. Feature contribution analysis indicated that wound length-related indicators, vitreous status and retina-related indicators contributed highly to visual outcomes. CONCLUSIONS: VisionGo has achieved an accurate and reliable prediction in visual outcome after vitrectomy for OGI-NLP eyes.

Comparing the impact of three-dimensional digital visualization technology versus traditional microscopy on microsurgeons in microsurgery: a prospective self-controlled study.

BACKGROUND: Emerging three-dimensional digital visualization technology (DVT) provides more advantages than traditional microscopy in microsurgery; however, its impact on microsurgeons' visual and nervous systems and delicate microsurgery is still unclear, which hinders the wider implementation of DVT in digital visualization for microsurgery. METHODS AND MATERIAL: Forty-two microsurgeons from the Zhongshan Ophthalmic Center were enrolled in this prospective self-controlled study. Each microsurgeon consecutively performed 30 min conjunctival sutures using a three-dimensional digital display and a microscope, respectively. Visual function, autonomic nerve activity, and subjective symptoms were evaluated before and immediately after the operation. Visual functions, including accommodative lag, accommodative amplitude, near point of convergence and contrast sensitivity function (CSF), were measured by an expert optometrist. Heart rate variability was recorded by a wearable device for monitoring autonomic nervous activity. Subjective symptoms were evaluated by questionnaires. Microsurgical performance was assessed by the video-based Objective Structured Assessment of Technical Skill (OSATS) tool. RESULTS: Accommodative lag decreased from 0.63 (0.18) diopters (D) to 0.55 (0.16) D ( P =0.014), area under the log contrast sensitivity function increased from 1.49 (0.15) to 1.52 (0.14) ( P =0.037), and heart rate variability decreased from 36.00 (13.54) milliseconds (ms) to 32.26 (12.35) ms ( P =0.004) after using the DVT, but the changes showed no differences compared to traditional microscopy ( P >0.05). No statistical significance was observed for global OSATS scores between the two rounds of operations [mean difference, 0.05 (95% CI: -1.17 to 1.08) points; P =0.95]. Subjective symptoms were quite mild after using both techniques. CONCLUSIONS: The impact of DVT-based procedures on microsurgeons includes enhanced accommodation and sympathetic activity, but the changes and surgical performance are not significantly different from those of microscopy-based microsurgery. Our findings indicate that short-term use of DVT is reliable for microsurgery and the long-term effect of using DVT deserve more consideration.

Incidence of and risk factors for suspected and definitive glaucoma after bilateral congenital cataract surgery: a 5-year follow-up.

AIMS: To report the incidence and associated risk factors for developing suspected and definitive glaucoma after bilateral congenital cataract (CC) removal with a 5-year follow-up. METHODS: Secondary analysis of a prospective longitudinal cohort study. Bilateral CC patients who had undergone cataract surgery between January 2011 and December 2014 at Zhongshan Ophthalmic Centre were recruited. Suspected glaucoma was defined as persistent ocular hypertension requiring medical treatment. Definitive glaucoma was defined as accompanied by the progression of glaucomatous clinical features. According to postoperative lens status in 5 years follow-up: 130 eyes in the aphakia group; 219 in the primary intraocular lens (IOL) implantation group and 337 in the secondary IOL implantation group. The Kaplan-Meier survival and Cox regression analyses were used to explore the cumulative incidence and risk factors for suspected and definitive glaucoma. RESULTS: Three hundred fifty-one children (686 eyes) with bilateral CCs were enrolled in the study. The mean age at surgery was 1.82±2.08 years, and the mean follow-up duration was 6.26±0.97 years. Suspected and definitive glaucoma developed at a mean time of 2.84±1.75 years (range 0.02-7.33 years) postoperatively. The cumulative incidence of suspected and definitive glaucoma was 9.97% (35 of 351 patients), including 6.12% (42 eyes) for definitive glaucoma and 2.48% (17 eyes) for suspected glaucoma. Microcornea (HR 4.103, p<0.0001), CC family history (HR 3.285, p=0.001) and initial anterior vitrectomy (HR 2.365 p=0.036) were risk factors for suspected and definitive glaucoma. Gender, age at surgery, intraocular surgery frequency, length of follow-up and frequency of neodymium-doped yttrium aluminumaluminium garnet laser were non-statistically significant. Primary IOL implantation was a protective factor (HR 0.378, p=0.007). CONCLUSIONS: Identifying suspected and definitive glaucoma after bilateral CC surgery can lower the risk of secondary blindness in children. Patients with related risk factors need to pay more attention and thus reach early intervention and treatment during clinical practice. Primary IOL implantation may be a potential protective factor, need more clinical trials to be verified. TRIAL REGISTRATION NUMBER: NCT04342052.

LensAge index as a deep learning-based biological age for self-monitoring the risks of age-related diseases and mortality.

Age is closely related to human health and disease risks. However, chronologically defined age often disagrees with biological age, primarily due to genetic and environmental variables. Identifying effective indicators for biological age in clinical practice and self-monitoring is important but currently lacking. The human lens accumulates age-related changes that are amenable to rapid and objective assessment. Here, using lens photographs from 20 to 96-year-olds, we develop LensAge to reflect lens aging via deep learning. LensAge is closely correlated with chronological age of relatively healthy individuals (R2 > 0.80, mean absolute errors of 4.25 to 4.82 years). Among the general population, we calculate the LensAge index by contrasting LensAge and chronological age to reflect the aging rate relative to peers. The LensAge index effectively reveals the risks of age-related eye and systemic disease occurrence, as well as all-cause mortality. It outperforms chronological age in reflecting age-related disease risks (p < 0.001). More importantly, our models can conveniently work based on smartphone photographs, suggesting suitability for routine self-examination of aging status. Overall, our study demonstrates that the LensAge index may serve as an ideal quantitative indicator for clinically assessing and self-monitoring biological age in humans.

Deep Learning Performance of Ultra-Widefield Fundus Imaging for Screening Retinal Lesions in Rural Locales.

Importance: Retinal diseases are the leading cause of irreversible blindness worldwide, and timely detection contributes to prevention of permanent vision loss, especially for patients in rural areas with limited medical resources. Deep learning systems (DLSs) based on fundus images with a 45° field of view have been extensively applied in population screening, while the feasibility of using ultra-widefield (UWF) fundus image-based DLSs to detect retinal lesions in patients in rural areas warrants exploration. Objective: To explore the performance of a DLS for multiple retinal lesion screening using UWF fundus images from patients in rural areas. Design, Setting, and Participants: In this diagnostic study, a previously developed DLS based on UWF fundus images was used to screen for 5 retinal lesions (retinal exudates or drusen, glaucomatous optic neuropathy, retinal hemorrhage, lattice degeneration or retinal breaks, and retinal detachment) in 24 villages of Yangxi County, China, between November 17, 2020, and March 30, 2021. Interventions: The captured images were analyzed by the DLS and ophthalmologists. Main Outcomes and Measures: The performance of the DLS in rural screening was compared with that of the internal validation in the previous model development stage. The image quality, lesion proportion, and complexity of lesion composition were compared between the model development stage and the rural screening stage. Results: A total of 6222 eyes in 3149 participants (1685 women [53.5%]; mean [SD] age, 70.9 [9.1] years) were screened. The DLS achieved a mean (SD) area under the receiver operating characteristic curve (AUC) of 0.918 (0.021) (95% CI, 0.892-0.944) for detecting 5 retinal lesions in the entire data set when applied for patients in rural areas, which was lower than that reported at the model development stage (AUC, 0.998 [0.002] [95% CI, 0.995-1.000]; P < .001). Compared with the fundus images in the model development stage, the fundus images in this rural screening study had an increased frequency of poor quality (13.8% [860 of 6222] vs 0%), increased variation in lesion proportions (0.1% [6 of 6222]-36.5% [2271 of 6222] vs 14.0% [2793 of 19 891]-21.3% [3433 of 16 138]), and an increased complexity of lesion composition. Conclusions and Relevance: This diagnostic study suggests that the DLS exhibited excellent performance using UWF fundus images as a screening tool for 5 retinal lesions in patients in a rural setting. However, poor image quality, diverse lesion proportions, and a complex set of lesions may have reduced the performance of the DLS; these factors in targeted screening scenarios should be taken into consideration in the model development stage to ensure good performance.

DeepQuality improves infant retinopathy screening.

Image quality variation is a prominent cause of performance degradation for intelligent disease diagnostic models in clinical applications. Image quality issues are particularly prominent in infantile fundus photography due to poor patient cooperation, which poses a high risk of misdiagnosis. Here, we developed a deep learning-based image quality assessment and enhancement system (DeepQuality) for infantile fundus images to improve infant retinopathy screening. DeepQuality can accurately detect various quality defects concerning integrity, illumination, and clarity with area under the curve (AUC) values ranging from 0.933 to 0.995. It can also comprehensively score the overall quality of each fundus photograph. By analyzing 2,015,758 infantile fundus photographs from real-world settings using DeepQuality, we found that 58.3% of them had varying degrees of quality defects, and large variations were observed among different regions and categories of hospitals. Additionally, DeepQuality provides quality enhancement based on the results of quality assessment. After quality enhancement, the performance of retinopathy of prematurity (ROP) diagnosis of clinicians was significantly improved. Moreover, the integration of DeepQuality and AI diagnostic models can effectively improve the model performance for detecting ROP. This study may be an important reference for the future development of other image-based intelligent disease screening systems.

Novel compound heterozygous variants of the SEC23A gene in a Chinese family with cranio-lenticulo-sutural dysplasia based on data from a large cohort of congenital cataract patients.

BACKGROUND: Cranio-lenticulo-sutural dysplasia (CLSD) is a rare dysmorphic syndrome characterized by skeletal dysmorphism, late-closing fontanels, and cataracts. CLSD is caused by mutations in the SEC23A gene (OMIM# 607812) and can be inherited in either an autosomal dominant or autosomal recessive pattern. To date, only four mutations have been reported to cause CLSD. This study aims to identify the disease-causing variants in a large cohort of congenital cataract patients, to expand the genotypic and phenotypic spectrum of CLSD, and to confirm the association between SEC23A and autosomal recessive CLSD (ARCLSD). METHODS: We collected detailed medical records and performed comprehensive ocular examinations and whole-exome sequencing (WES) on 115 patients with congenital cataracts. After suspecting that a patient may have CLSD based on the sequencing results, we proceeded to conduct transmission electron microscopy (TEM) on the cultured skin fibroblasts. The clinical validity of the reported gene-disease relationships for the gene and the disease was evaluated using the ClinGen gene curation framework. RESULTS: Two novel compound heterozygous variants (c.710A > C p.Asp237Ala, c.1946T > C p.Leu649Pro) of the SEC23A gene, classified as variant of uncertain significance, were identified in the proband with skeletal, cardiac, ocular, and hearing defects. The observation of typical distended endoplasmic reticulum cisternae further supported the diagnosis of CLSD. Application of the ClinGen gene curation framework confirmed the association between SEC23A and ARCLSD. CONCLUSION: This study expands the genotypic and phenotypic spectrum of CLSD, proposes TEM as a supplemental diagnostic method, and indicates that congenital cataracts are a typical sign of ARCLSD.

Protocol to analyze fundus images for multidimensional quality grading and real-time guidance using deep learning techniques.

Data quality issues have been acknowledged as one of the greatest obstacles in medical artificial intelligence research. Here, we present DeepFundus, which employs deep learning techniques to perform multidimensional classification of fundus image quality and provide real-time guidance for on-site image acquisition. We describe steps for data preparation, model training, model inference, model evaluation, and the visualization of results using heatmaps. This protocol can be implemented in Python using either the suggested dataset or a customized dataset. For complete details on the use and execution of this protocol, please refer to Liu et al.1.

An artificial intelligence system for the whole process from diagnosis to treatment suggestion of ischemic retinal diseases.

Ischemic retinal diseases (IRDs) are a series of common blinding diseases that depend on accurate fundus fluorescein angiography (FFA) image interpretation for diagnosis and treatment. An artificial intelligence system (Ai-Doctor) was developed to interpret FFA images. Ai-Doctor performed well in image phase identification (area under the curve [AUC], 0.991-0.999, range), diabetic retinopathy (DR) and branch retinal vein occlusion (BRVO) diagnosis (AUC, 0.979-0.992), and non-perfusion area segmentation (Dice similarity coefficient [DSC], 89.7%-90.1%) and quantification. The segmentation model was expanded to unencountered IRDs (central RVO and retinal vasculitis), with DSCs of 89.2% and 83.6%, respectively. A clinically applicable ischemia index (CAII) was proposed to evaluate ischemic degree; patients with CAII values exceeding 0.17 in BRVO and 0.08 in DR may be associated with increased possibility for laser therapy. Ai-Doctor is expected to achieve accurate FFA image interpretation for IRDs, potentially reducing the reliance on retinal specialists.